Paper Info
Title
Incorporating terminology evolution for query translation in text retrieval with association rules
Abstract
Time-stamped documents such as newswire articles, blog posts and other web-pages are often archived online. When these archives cover long spans of time, the terminology within them could undergo significant changes. Hence, when users pose queries pertaining to historical information, over such documents, the queries need to be translated, taking into account these temporal changes, to provide accurate responses to users. For example, a query on Sri Lanka should automatically retrieve documents with its former name Ceylon. We call such concepts SITACs, i.e., Semantically Identical Temporally Altering Concepts. In order to discover SITACs, we propose an approach based on a novel framework constituting an integration of natural language processing, association rule mining, and contextual similarity as a learning technique. The proposed approach has been experimented with real data and has been found to yield good results with respect to efficiency and accuracy.
Year
DOI
Venue
2010
10.1145/1871437.1871730
CIKM
Keywords
Field
DocType
semantically identical temporally altering,concepts sitacs,text retrieval,time-stamped document,accurate response,association rule mining,blog post,query translation,sri lanka,archived online,terminology evolution,contextual similarity,association rule,association rules,web pages,natural language processing,ranking
Data mining,Ranking,Information retrieval,Terminology,Computer science,Ceylon,Former name,Association rule learning,Artificial intelligence,Natural language processing,Text retrieval
Conference
Citations 
PageRank 
References 
17
0.90
6
Authors
6
Name
Order
Citations
PageRank
Amal C. Kaluarachchi1170.90
Aparna S. Varde218828.71
Srikanta Bedathur360743.23
Gerhard Weikum4127102146.01
Jing Peng514516.57
Anna Feldman6378.09